Natural Language Understanding

Transcription

1 Natural Language Understanding Lecture 16: Entity-based Coherence Mirella Lapata School of Informatics University of Edinburgh March 28, 2017 Mirella Lapata Natural Language Understanding 1

2 1 2 Discourse Representation Entity Transitions Ranking Model 3 Text Ordering Summarization Reading: Barzilay and Lapata (2008). Mirella Lapata Natural Language Understanding 2

3 Coherence in Text Coherence: is a property of well-written texts; makes them easier to read and understand; ensures that sentences are meaningfully related; and that the reader can work out what expressions mean; the text is thematically organized; temporally organized; rather than a random concatenation of sentences. In this lecture, we will discuss Barzilay and Lapata s (2008) entity-based model of coherence. Mirella Lapata Natural Language Understanding 3

4 Coherence in Text Summary A Britain said he did not have diplomatic immunity. The Spanish authorities contend that Pinochet may have committed crimes against Spanish citizens in Chile. Baltasar Garzon filed a request on Wednesday. Chile said, President Fidel Castro said Sunday he disagreed with the arrest in London. Summary B Former Chilean dictator Augusto Pinochet, was arrested in London on 14 October Pinochet, 82, was recovering from surgery. The arrest was in response to an extradition warrant served by a Spanish judge. Pinochet was charged with murdering thousands, including many Spaniards. Pinochet is awaiting a hearing, his fate in the balance. American scholars applauded the arrest. Mirella Lapata Natural Language Understanding 4

5 Entity-based Coherence The way entities are introduced and discussed influences coherence (Grosz et al., 1995). Entities in an utterance are ranked according to salience. Is an entity pronominalized or not? Is an entity in a prominent syntactic position? Each utterance has one center ( topic or focus). Coherent discourses have utterances with common centers. Entity transitions capture degrees of coherence (e.g., in Centering theory continue > shift). Notions of salience, utterance, ranking are left unspecified. Mirella Lapata Natural Language Understanding 5

6 Entity-based Local Coherence John went to his favorite music store to buy a piano. He had frequented the store for many years. He was excited that he could finally buy a piano. He arrived just as the store was closing for the day. John went to his favorite music store to buy a piano. It was a store John had frequented for many years. He was excited that he could finally buy a piano. It was closing just as John arrived. Mirella Lapata Natural Language Understanding 6

7 Entity-based Local Coherence John went to his favorite music store to buy a piano. He had frequented the store for many years. He was excited that he could finally buy a piano. He arrived just as the store was closing for the day. John went to his favorite music store to buy a piano. It was a store John had frequented for many years. He was excited that he could finally buy a piano. It was closing just as John arrived. Mirella Lapata Natural Language Understanding 6

8 Discourse Representation Entity Transitions Ranking Model Can we compute a discourse representation automatically? Does it capture coherence characteristics? What linguistic information matters for coherence? Is it robust across domains and genres? What is an appropriate coherence model? View coherence rating as a machine learning problem. Learn a ranking function without manual involvement. Apply to text-to-text generation tasks. Inspired from entity-based theories, not a direct implementation of any theory in particular. Mirella Lapata Natural Language Understanding 7

9 Discourse Representation Entity Transitions Ranking Model 1 Former Chilean dictator Augusto Pinochet, was arrested in London on 14 October Pinochet, 82, was recovering from surgery. 3 The arrest was in response to an extradition warrant served by a Spanish judge. 4 Pinochet was charged with murdering thousands, including many Spaniards. 5 He is awaiting a hearing, his fate in the balance. 6 American scholars applauded the arrest. Mirella Lapata Natural Language Understanding 8

10 Discourse Representation Entity Transitions Ranking Model Former Chilean dictator Augusto Pinochet, was arrested in London on 14 October Pinochet, 82, was recovering from surgery. The arrest was in response to an extradition warrant served by a Spanish judge. Pinochet was charged with murdering thousands, including many Spaniards. He is awaiting a hearing, Pinochet s fate in the balance. American scholars applauded the arrest. Mirella Lapata Natural Language Understanding 9

11 Discourse Representation Entity Transitions Ranking Model Former Chilean dictator Augusto Pinochet S, was arrested in London X on 14 October X Pinochet S, 82, was recovering from surgery X. The arrest S was in response X to an extradition warrant X served by a Spanish judge S. Pinochet O was charged with murdering thousands O, including many Spaniards O. Pinochet S is awaiting a hearing O, his fate X in the balance X. American scholars S applauded the arrest O. Mirella Lapata Natural Language Understanding 9

12 Discourse Representation Entity Transitions Ranking Model 1 Pinochet S London X October X 2 Pinochet S surgery X 3 arrest S response X warrant X judge O 4 Pinochet O thousands O Spaniards O 5 Pinochet S hearing O Pinochet X fate X balance X 6 scholars S arrest O Mirella Lapata Natural Language Understanding 10

13 Discourse Representation Entity Transitions Ranking Model Pinochet London October Surgery Arrest Warrant Judge Thousands Spaniards Hearing Fate Balance Scholars Mirella Lapata Natural Language Understanding 11

14 Discourse Representation Entity Transitions Ranking Model Pinochet London October Surgery Arrest Extradition Warrant Judge Thousands Spaniards Hearing Fate Balance Scholars 1 S 2 S 3 4 O 5 S 6 Mirella Lapata Natural Language Understanding 11

15 Discourse Representation Entity Transitions Ranking Model Pinochet London October Surgery Arrest Extradition Warrant Judge Thousands Spaniards Hearing Fate Balance 1 S X X 2 S X 3 S X X O 4 O O O 5 S O X X 6 O S Scholars Mirella Lapata Natural Language Understanding 11

16 Discourse Representation Entity Transitions Ranking Model S X X S X S X X O O O O S O X X O S Mirella Lapata Natural Language Understanding 12

17 Discourse Representation Entity Transitions Ranking Model S X X S X S X X O O O O S O X X O S S S X X X X X X O O O O X X O Mirella Lapata Natural Language Understanding 12

18 Discourse Representation Entity Transitions Ranking Model S X X S X S X X O O O O S O X X O S S S X X X X X X O O O O X X O Mirella Lapata Natural Language Understanding 12

19 Entity Transitions Discourse Representation Entity Transitions Ranking Model Definition A local entity transition is a sequence {S, O, X, } n that represents entity occurrences and their syntactic roles in n adjacent sentences. Feature Vector Notation Each grid x ij for document d i is represented by a feature vector: Φ(x ij ) = (p 1 (x ij ), p 2 (x ij ),..., p m (x ij )) m is the number of predefined entity transitions p t (x ij ) the probability of transition t in grid x ij Mirella Lapata Natural Language Understanding 13

20 Entity Transitions Discourse Representation Entity Transitions Ranking Model Example (transitions of length 2) S S S O S X S O S O O O X O X S X O X X X S O X d d d Mirella Lapata Natural Language Understanding 14

21 Entity Transitions Discourse Representation Entity Transitions Ranking Model Example (transitions of length 2) S S S O S X S O S O O O X O X S X O X X X S O X d d d Mirella Lapata Natural Language Understanding 14

22 Linguistic Dimensions Discourse Representation Entity Transitions Ranking Model Salience: Are some entities more important than others? Discriminate between salient (frequent) entities and the rest. Collect statistics separately for each group. Coreference: What is its contribution? Entities are coreferent if they have the same surface form. Apply a coreference resolution system. Syntax: Does syntactic knowledge matter? Use four categories {S, O, X, }. Reduce categories to {X, }. Mirella Lapata Natural Language Understanding 15

23 Learning a Ranking Function Discourse Representation Entity Transitions Ranking Model Training Set Ordered pairs (x ij, x ik ), where x ij and x ik represent the same document d i, and x ij is more coherent than x ik (assume j > k). Goal Find a parameter vector w such that: w (Φ(x ij ) Φ(x ik )) > 0 j, i, k such that j > k Support Vector Machines Constraint optimization problem can be solved using the search technique described in Joachims (2002). Mirella Lapata Natural Language Understanding 16

24 Text Ordering Text Ordering Summarization Motivation Determine a sequence in which to present a set of items. Essential step in generation applications. Data Source document and permutations of its sentences. Original order assumed coherent. Given k documents, with n permutations, obtain k n pairwise rankings for training and testing. Two corpora, Earthquakes and Accidents, 100 texts each. Mirella Lapata Natural Language Understanding 17

25 Text Ordering Text Ordering Summarization Sentence 1 Sentence 2 Sentence 3 Sentence 4 Mirella Lapata Natural Language Understanding 18

26 Text Ordering Text Ordering Summarization Sentence 1 Sentence 2 Sentence 3 Sentence 4 Sentence 2 Sentence 3 Sentence 4 Sentence 1 Sentence 4 Sentence 3 Sentence 2 Sentence 1 Sentence 2 Sentence 1 Sentence 4 Sentence 3 Mirella Lapata Natural Language Understanding 18

27 Comparison with State of the Art Text Ordering Summarization Vector-based Model (LSA, Foltz et al., 1998): Meaning of individual words is represented in vector space. Sentence meaning is the mean of the vectors of its words. Average distance of adjacent sentences. Unsupervised, local, lexicalized, domain independent. Mirella Lapata Natural Language Understanding 19

28 Comparison with State of the Art Text Ordering Summarization x x S 5 S 4 S 3 S 2 S 1 S 5 S 4 S 3 S 2 y S 1 y Mirella Lapata Natural Language Understanding 20

29 Comparison with State of the Art Text Ordering Summarization HMM-based Content Models (Barzilay and Lee, 2004): Model topics and their order in texts. Model is an HMM: states correspond to topics ( sentences). Model selects sentence order with highest probability. Supervised, global, lexicalized, domain dependent. Mirella Lapata Natural Language Understanding 21

30 Comparison with State of the Art Text Ordering Summarization Casualties Location Strength Rescue History the quake its was magnitude near was San Jose... Mirella Lapata Natural Language Understanding 22

31 Text Ordering Summarization Mirella Lapata Natural Language Understanding 23

32 Results: Ordering Text Ordering Summarization % ranks correct (test set) Cref+Syn+Sal -Cref-Syn-Sal +Cref+Syn+Sal +Cref-Syn-Sal HMM LSA Earthquakes % ranks correct (test set) Cref+Syn+Sal -Cref-Syn-Sal +Cref+Syn+Sal +Cref-Syn-Sal HMM LSA Accidents Mirella Lapata Natural Language Understanding 24

33 Discussion Text Ordering Summarization Omission of coreference causes performance drop. Syntax and Salience have more effect on Accidents corpus. Linguistically poor model generally worse. Entity model is better than LSA. HMM-based content models exhibit high variability. Models seem to be complementary. Mirella Lapata Natural Language Understanding 25

34 Summarization Text Ordering Summarization Motivation Summaries naturally exhibit coherence violations. Compare model against rankings elicited by human judges. Useful for automatic evaluation of machine generated text. Data Outputs of 5 multi-document summarization systems and corresponding human authored summaries (DUC 2003). Participants assign readability score on a seven point scale. 144 summaries, 177 participants (23 per summary). Mirella Lapata Natural Language Understanding 26

35 Results: Summarization Text Ordering Summarization 90 -Cref+Syn+Sal -Cref-Syn-Sal +Cref+Syn+Sal +Cref-Syn-Sal LSA Summaries % ranks correct (test set) Mirella Lapata Natural Language Understanding 27

36 Results Text Ordering Summarization Coreference decreases accuracy (machine generated texts). Salience seems to have more of an impact here. Linguistically poor model is generally worse. Entity model performs better than LSA. LSA is unsupervised and exposed only to human texts. Training corpus is unsuitable for HMM-based content models. Mirella Lapata Natural Language Understanding 28

37 Summary Text Ordering Summarization Strengths: Novel framework for representing and measuring coherence. Entity grid and cross-sentential transitions. Suited for learning appropriate ranking function. Fully automatic and robust, useful for system development. Weaknesses: Entity grid doesn t contain lexical information. Doesn t contain a notion of global coherence. Can t model multi-paragraph text. Mirella Lapata Natural Language Understanding 29